Method and system for maintaining privacy in scoring of consumer spending behavior

ABSTRACT

A method for maintaining consumer privacy in behavioral scoring includes a first computing system and a second computing system. The first computing system disguises consumer characteristics and maps disguised consumer characteristics to unencrypted account identifiers, and then transmits the data to the second computing system. The second computing system encrypts the account identifiers upon receipt, and maps the encrypted account identifiers to anonymous transaction data. The second computing system uses the transaction data to calculate consumer behavioral scores, and then generates a scoring algorithm that uses disguised consumer characteristics to calculate consumer behavior scores based on the calculated consumer behavioral scores and corresponding disguised consumer characteristics. The generated algorithm is then returned to the first computing system, with the second computing system not receiving any unencrypted account identifiers, any undisguised consumer characteristics, or any personally identifiable information.

FIELD

The present disclosure relates to the maintaining of consumer privacy inbehavioral scoring, specifically the generating and use of scoringalgorithms that can maintain privacy via the use of disguised consumercharacteristics.

BACKGROUND

Many entities, such as merchants, advertisers, content providers,manufacturers, and more, are often greatly interested in finding out asmuch as they can about consumers. By learning more about consumers,these entities can often better target advertisements, offers, or othercontent to consumers, or better select consumers for receipt of specificcontent. As a result, these entities may try to obtain information onconsumers as often as possible, and with as much detail included aspossible. One such piece of information is transaction data associatedwith payment transactions involving a consumer. Such information may beuseful for identifying a consumer's purchasing behavior and shoppingtrends.

However, consumers may be worried about the amount of information thatadvertisers and other such entities may possess about themselves,particularly when it comes to their shopping behavior. As a result, manyregulations have been passed and/or adopted that may limit an entity'sability to gather and/or possess personally identifiable informationassociated with a particular consumer. Therefore, entities are now oftenin need for information about consumers that can provide valuabledetail, while still maintaining a consumer's privacy as per regulationsand requirements.

Thus, there is a need for a technical solution to provide behavioralscores for consumers based on transaction data while maintainingconsumer privacy.

SUMMARY

The present disclosure provides a description of systems and methods forthe maintaining of consumer privacy in behavioral scoring.

A method for maintaining consumer privacy in behavioral scoringincludes: storing, in a memory of a first computing system, a pluralityof account identifiers, wherein each account identifier is associatedwith a payment account corresponding to a consumer; receiving, by areceiver of the first computing system, transmitted data, wherein thetransmitted data includes at least a behavior prediction request and adata file including at least a plurality of first encrypted accountidentifiers, wherein each first encrypted account identifier isencrypted using a first one-way encryption and is associated with apayment account corresponding to a consumer, and further including, foreach first encrypted account identifier, a set of consumercharacteristics associated with the consumer corresponding to theassociated payment account; disguising, by a processor of the firstcomputing system, each set of consumer characteristics included in thereceived data file such that the respective set of consumercharacteristics is not personally identifiable; mapping, in the memoryof the first computing system, each of the plurality of first encryptedaccount identifiers and corresponding disguised set of consumercharacteristics to an account identifier of the plurality of accountidentifiers; transmitting, by a transmitter of the first computingsystem, at least each account identifier and mapped first encryptedaccount identifier and corresponding disguised set of consumercharacteristics to a second computing system, wherein a receiver of thesecond computing system is configured to encrypt each account identifierinto a second encrypted account identifier using a second one-wayencryption upon receipt; receiving, by the receiver of the secondcomputing system, a plurality of transaction data entries, wherein eachtransaction data entry includes data related to a payment transactionincluding at least a second encrypted account identifier and transactiondata; generating, by a processor of the second computing system, analgorithm configured to calculate a behavior prediction scorecorresponding to the behavior prediction request using disguisedconsumer characteristic values, wherein the generated algorithm is basedon at least the transaction data included in each received transactiondata entry and the disguised set of consumer characteristics mapped tothe second encrypted account identifier included in the respectivetransaction data entry, wherein the second computing system does notreceive any unencrypted account identifiers, any undisguised consumercharacteristics, or any personally identifiable information.

Another method for maintaining consumer privacy in behavioral scoringincludes: storing, in a memory of a first computing system, a pluralityof account identifiers, wherein each account identifier is associatedwith a payment account corresponding to a consumer; receiving, by areceiver of a first computing system, transmitted data, wherein thetransmitted data includes at least a behavior prediction request and adata file including at least a plurality of first encrypted accountidentifiers, wherein each first encrypted account identifier isencrypted using a first one-way encryption and is associated with apayment account corresponding to a consumer, and further including, foreach first encrypted account identifier, a set of consumercharacteristics associated with the consumer corresponding to theassociated payment account; disguising, by a processor of the firstcomputing system, each set of consumer characteristics included in thereceived data file such that the respective set of consumercharacteristics is not personally identifiable; mapping, in the memoryof the first computing system, each of the plurality of first encryptedaccount identifiers and corresponding disguised set of consumercharacteristics to an account identifier of the plurality of accountidentifiers; transmitting, by a transmitter of the first computingsystem, at least each account identifier and mapped first encryptedaccount identifier to a second computing system, wherein a receiver ofthe second computing system is configured to encrypt each accountidentifier into a second encrypted account identifier using a secondone-way encryption upon receipt; receiving, by the receiver of thesecond computing system, a plurality of transaction data entries,wherein each transaction data entry includes data related to a paymenttransaction including at least a second encrypted account identifier andtransaction data; calculating, by a processor of the second computingsystem, a behavior prediction score corresponding to the behaviorprediction request for each second encrypted account identifier based onat least the transaction data included in each transaction data entryincluding the respective second encrypted account identifier; andtransmitting, by a transmitter of the second computing system, thecalculated behavior prediction score for each second encrypted accountidentifier, wherein the second computing system does not receive anyunencrypted account identifiers, any undisguised consumercharacteristics, or any personally identifiable information.

A system for maintaining consumer privacy in behavioral predictionscoring includes a first computing system and a second computing system.The first computing system includes: a memory configured to store aplurality of account identifiers, wherein each account identifier isassociated with a payment account corresponding to a consumer; areceiver configured to receive transmitted data, wherein the transmitteddata includes at least a behavior prediction request and a data fileincluding at least a plurality of first encrypted account identifiers,wherein each first encrypted account identifier is encrypted using afirst one-way encryption and is associated with a payment accountcorresponding to a consumer, and further including, for each firstencrypted account identifier, a set of consumer characteristicsassociated with the consumer corresponding to the associated paymentaccount; a processor; and a transmitter. The processor is configured to:disguise each set of consumer characteristics included in the receiveddata file such that the respective set of consumer characteristics isnot personally identifiable; and map each of the plurality of firstencrypted account identifiers and corresponding disguised set ofconsumer characteristics to an account identifier of the plurality ofaccount identifiers. The transmitter is configured to transmit at leasteach account identifier and mapped first encrypted account identifierand corresponding disguised set of consumer characteristics to a secondcomputing system, wherein a receiver of the second computing system isconfigured to encrypt each account identifier into a second encryptedaccount identifier using a second one-way encryption upon receipt. Thesecond computing system includes: a receiver configured to receive aplurality of transaction data entries, wherein each transaction dataentry includes data related to a payment transaction including at leasta second encrypted account identifier and transaction data; and aprocessor configured to generate an algorithm configured to calculate abehavior prediction score corresponding to the behavior predictionrequest using disguised consumer characteristic values, wherein thegenerated algorithm is based on at least the transaction data includedin each received transaction data entry and the disguised set ofconsumer characteristics mapped to the second encrypted accountidentifier included in the respective transaction data entry. The secondcomputing system does not receive any unencrypted account identifiers,any undisguised consumer characteristics, or any personally identifiableinformation.

Another system for maintaining consumer privacy in behavioral scoringincludes a first computing system and a second computing system. Thefirst computing system includes: a memory configured to store aplurality of account identifiers, wherein each account identifier isassociated with a payment account corresponding to a consumer; areceiver configured to receive transmitted data, wherein the transmitteddata includes at least a behavior prediction request and a data fileincluding at least a plurality of first encrypted account identifiers,wherein each first encrypted account identifier is encrypted using afirst one-way encryption and is associated with a payment accountcorresponding to a consumer, and further including, for each firstencrypted account identifier, a set of consumer characteristicsassociated with the consumer corresponding to the associated paymentaccount; a processor; and a transmitter. The processor is configured to:disguise each set of consumer characteristics included in the receiveddata file such that the respective set of consumer characteristics isnot personally identifiable; and map, in the memory of the firstcomputing system, each of the plurality of first encrypted accountidentifiers and corresponding disguised set of consumer characteristicsto an account identifier of the plurality of account identifiers. Thetransmitter is configured to transmit at least each account identifierand mapped first encrypted account identifier to a second computingsystem, wherein a receiver of the second computing system is configuredto encrypt each account identifier into a second encrypted accountidentifier using a second one-way encryption upon receipt. The secondcomputing system includes: a receiver configured to receive a pluralityof transaction data entries, wherein each transaction data entryincludes data related to a payment transaction including at least asecond encrypted account identifier and transaction data; a processorconfigured to calculate a behavior prediction score corresponding to thebehavior prediction request for each second encrypted account identifierbased on at least the transaction data included in each transaction dataentry including the respective second encrypted account identifier, anda transmitter configured to transmit the calculated behavior predictionscore for each second encrypted account identifier. The second computingsystem does not receive any unencrypted account identifiers, anyundisguised consumer characteristics, or any personally identifiableinformation.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The scope of the present disclosure is best understood from thefollowing detailed description of exemplary embodiments when read inconjunction with the accompanying drawings. Included in the drawings arethe following figures:

FIG. 1 is a high level architecture illustrating a system formaintaining consumer privacy in behavioral scoring in accordance withexemplary embodiments.

FIG. 2 is a block diagram illustrating a computing system for use in thesystem of FIG. 1 for the generation of behavioral scoring algorithmswhile maintaining consumer privacy in accordance with exemplaryembodiments.

FIGS. 3A and 3B are flow diagrams illustrating a process for generatingconsumer behavioral scores while maintaining consumer privacy using twocomputing systems of the system 100 of FIG. 1, in accordance withexemplary embodiments.

FIGS. 4A-4C are flow diagrams illustrating a process for generatingconsumer behavioral scores while maintaining consumer privacy usingthree computing systems of the system 100 of FIG. 1, in accordance withexemplary embodiments.

FIGS. 5 and 6 are flow charts illustrating exemplary methods formaintaining consumer privacy in behavioral scoring in accordance withexemplary embodiments.

FIG. 7 is a block diagram illustrating a computer system architecture inaccordance with exemplary embodiments.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description of exemplary embodiments areintended for illustration purposes only and are, therefore, not intendedto necessarily limit the scope of the disclosure.

DETAILED DESCRIPTION Glossary of Terms

Payment Network—A system or network used for the transfer of money viathe use of cash-substitutes. Payment networks may use a variety ofdifferent protocols and procedures in order to process the transfer ofmoney for various types of transactions. Transactions that may beperformed via a payment network may include product or servicepurchases, credit purchases, debit transactions, fund transfers, accountwithdrawals, etc. Payment networks may be configured to performtransactions via cash-substitutes, which may include payment cards,letters of credit, checks, financial accounts, etc. Examples of networksor systems configured to perform as payment networks include thoseoperated by MasterCard®, VISA®, Discover®, American Express®, PayPal®,etc. Use of the term “payment network” herein may refer to both thepayment network as an entity, and the physical payment network, such asthe equipment, hardware, and software comprising the payment network.

Personally identifiable information (PII)—PII may include informationthat may be used, alone or in conjunction with other sources, touniquely identify a single individual. Information that may beconsidered personally identifiable may be defined by a third party, suchas a governmental agency (e.g., the U.S. Federal Trade Commission, theEuropean Commission, etc.), a non-governmental organization (e.g., theElectronic Frontier Foundation), industry custom, consumers (e.g.,through consumer surveys, contracts, etc.), codified laws, regulations,or statutes, etc. The present disclosure provides for methods andsystems where the processing system 102 does not possess any personallyidentifiable information. Systems and methods apparent to persons havingskill in the art for rendering potentially personally identifiableinformation anonymous may be used, such as bucketing. Bucketing mayinclude aggregating information that may otherwise be personallyidentifiable (e.g., age, income, etc.) into a bucket (e.g., grouping) inorder to render the information not personally identifiable. Forexample, a consumer of age 26 with an income of $65,000, which mayotherwise be unique in a particular circumstance to that consumer, maybe represented by an age bucket for ages 21-30 and an income bucket forincomes $50,000 to $74,999, which may represent a large portion ofadditional consumers and thus no longer be personally identifiable tothat consumer. In other embodiments, encryption may be used. Forexample, personally identifiable information (e.g., an account number)may be encrypted (e.g., using a one-way encryption) such that theprocessing system 102 may not possess the PII or be able to decrypt theencrypted PII.

System for Maintaining Privacy in Behavioral Scoring

FIG. 1 illustrates a system 100 for the maintaining of privacy ingenerating behavioral scoring algorithms and application thereof toconsumer characteristic data.

The system 100 may include a processing system 102. The processingsystem 102 may be configured to generate scoring algorithms configuredto generate behavioral scores based on consumer characteristics usingthe methods and systems discussed herein. The consumer characteristicsmay be demographics, such as age, gender, income, residential status,marital status, familial status, zip code, postal code, occupation,education, etc. or other suitable types of characteristics that may beassociated with one or more consumers. The generated behavioral scoresmay be based on the consumer characteristics and may be indicative ofthe associated consumer or consumers' propensity for a certain type ofpurchase behavior.

For example, the processing system 102 may generate a scoring algorithmconfigured to score a consumer for their propensity to purchaseelectronic goods in the next thirty days, based on their consumercharacteristics. The types of purchase behavior that may be indicated bya score calculated using a scoring algorithm generated using the methodsand systems discussed herein may include propensities to spend at aspecific merchant or merchants, in a specific industry or industries, ata specific geographic location, during a specified period of time,within a specified range of transaction amounts, any combinationthereof, and any other behaviors that will be apparent to persons havingskill in the relevant art.

In exemplary embodiments, the processing system 102 may include two ormore computing systems and may be configured such that no individualcomputing system included in the processing system 102 may possessunencrypted transaction data and personally identifiable information atthe same time. In a first embodiment, discussed in more detail below,the processing system 102 may include a first computing system 106 and asecond computing system 108. In another embodiment, also discussed inmore detail below, the processing system 102 may include a firstcomputing system 106, a second computing system 108, and a thirdcomputing system 110. The computing systems 106, 108, and 110 discussedin more detail below, may be configured to perform the steps disclosedherein for the generation of scoring algorithms and the calculation ofbehavioral scores.

The system 100 may further include a requesting entity 104. Therequesting entity 104 may be an entity that is requesting a scoringalgorithm for calculating behavioral scores, or the behavioral scoresthemselves, for a plurality of consumers. The requesting entity 104 maytransmit data to the processing system 102 (e.g., to be received by thefirst computing system 106, which may not possess any personallyidentifiable information), which may include consumer characteristicsfor each of a plurality of consumers, and encrypted account identifiersfor each of the plurality of consumers.

In some embodiments, the processing system 102 may be configured todisguise the consumer characteristics to anonymize the data such that itmay not be personally identifiable. In one such embodiment, the firstcomputing system 106 may disguise the variables and/or values for eachof the consumer characteristics. For example, the consumercharacteristics may include demographics, including a gender variablethat has a value of male or female for each consumer. The firstcomputing system 106 may, after the data has been received from therequesting entity 104, disguise both the variable, gender, and thevalues, male or female, such that the information may not be used topersonally identify the related consumer, such as replacing the gendervariable with “X1” and replacing the male and female values with “A” and“B,” respectively.

The disguised values may then be used by the second computing system 108and/or third computing system 110 to generate a scoring algorithm basedon transaction data. Transaction data may be received from a paymentnetwork 112. The payment network 112 may receive and store transactiondata as part of the processing of payment transactions using methods andsystems that will be apparent to persons having skill in the relevantart. The payment network 112 may provide the transaction data to theprocessing system 102, which may be received by one of the includedcomputing systems. In an exemplary embodiment, the transaction data maybe received by the second computing system 108 and/or third computingsystem 110, but not the first computing system 106, such that nocomputing system in the processing system 102 possess both transactiondata and personally identifiable information. In such embodiments, thetransaction data may include only encrypted account identifiers and maynot include any unencrypted account information.

As discussed in more detail below, scoring algorithms may be generatedfor a specified type of consumer purchase behavior by the secondcomputing system 108 and/or third computing system 110 using thereceived transaction data, and may use disguised consumer characteristicvariables and values as part of the algorithm. For example, thealgorithm may use disguised variable X1 as an input for part of thescore calculation, and may expect a value of A or B for the variable.The scoring algorithm using the disguised characteristics as inputs maybe generated using transaction data for accounts and disguisedcharacteristics associated with each respective account. As discussed inmore detail below, the transaction data and disguised characteristicsmay be matched in the second computing system 108 and/or third computingsystem 110 using encrypted account identifiers such that no computingsystem in the processing system 102 possess both undisguisedcharacteristic data and transaction data.

The processing system 102 may be further configured to transmit agenerated scoring algorithm back to the requesting entity 104 inresponse to an earlier request. In instances where the requesting entity104 is requesting behavioral scores, the processing system 102 may firstscore each of the accounts for which characteristic data was receivedusing the generated scoring algorithm, and may return the scores to therequesting entity 104. In some embodiments, the first computing system106 may undisguise the characteristics included in the generatedalgorithm prior to transmission to the requesting entity 104.

Methods and systems discussed herein may thereby be enable to generatescoring algorithms using for identifying behavioral scores for consumersbased on consumer characteristics, while maintaining a high level ofconsumer privacy via the use of multiple computing systems. Bypartitioning the processing system 102 into multiple computing systems,none of which possess both transaction data and any personallyidentifiable information, the processing system 102 may be able toaccurately generate scoring algorithms without compromising consumerprivacy.

Computing Systems

FIG. 2 illustrates an embodiment of a computing system 200 of the system100. It will be apparent to persons having skill in the relevant artthat the embodiment of the computing system 200 illustrated in FIG. 2 isprovided as illustration only and may not be exhaustive to all possibleconfigurations of computing system 200 suitable for performing thefunctions as discussed herein. For example, the computer system 700illustrated in FIG. 7 and discussed in more detail below may be asuitable configuration of the computing system 200.

The computing system 200 illustrated in FIG. 2 and discussed herein maybe representative of each of the computing systems 106, 108, and 110included in the processing system 102. While it is discussed herein thateach of the computing systems 106, 108, and 110 may each include thesame components of the computing system 200 illustrated in FIG. 2, itwill be apparent to persons having skill in the relevant art that thecomputing systems 106, 108, and 110 may each include different and/oradditional components than those illustrated in the computing system200, and that each of the computing systems 106, 108, and 110 mayinclude configurations different from the computing system 200illustrated in FIG. 2.

The computing system 200 may include a receiving unit 202. The receivingunit 202 may be configured to receive data over one or more networks viaone or more network protocols. The receiving unit 202 may therebyreceive data from the requesting entity 104, which may include one ormore data files including encrypted account identifiers and consumercharacteristic data. Received data may also include behavior predictioncriteria and/or scoring requests. The receiving unit 202 in somecomputing systems 200 may also be configured to receive transaction datafrom the payment network 112. In some instances, received transactiondata may be associated with encrypted account identifiers and may notinclude any personally identifiable information.

In some computing systems 200, the receiving unit 202 may be configuredto encrypt data upon receipt. For instance, the receiving unit 202 ofthe second computing system 108 may be configured to encrypt accountidentifiers upon receipt such that the second computing system 108 doesnot possess any unencrypted account identifiers. In such an instance,the encryption may be performed by the receiving unit 202, or may beperformed by another component of the computing system 200 before thedata is made available to the rest of the system, such as an encryptingunit.

Encryption performed by the receiving unit 202 or other unit may be aone-way encryption, such that the encrypted data may not be unencrypted.Suitable encryption algorithms and methods of encryption that may beused will be apparent to persons having skill in the relevant art, suchas cryptographic hash functions (e.g., one or more of the SHA-2 set ofcryptographic hash functions). In some instances, a salt may also beused as part of the encryption.

The computing system 200 may also include a processing unit 204. Theprocessing unit 204 may be configured to perform the functions of eachcomputing system as discussed herein. In some instances, encryption maybe performed by the processing unit 204 in each respective computingsystem 200. The processing unit 204 may also be configured to identifyassociations in data received by the receiving unit 202 of therespective system. For instance, the processing unit 204 of the secondcomputing system 108 may, as discussed below, be configured to matchreceived transaction data with disguised consumer characteristics basedon associated encrypted account identifiers.

The processing unit 204 may also be configured to generate scoringalgorithms in some computing systems 200. Scoring algorithms may begenerated based on transaction data and associated disguised consumercharacteristics. In some embodiments, a scoring algorithm may begenerated to provide a score representing a specific consumer behavior,which may be a consumer behavior indicated in a request received by thereceiving unit 202 of a computing system 200 in the processing system102. The processing unit 204 may also be configured to calculatebehavior scores based on application of a generated scoring algorithm toa set of disguised or undisguised consumer characteristics.

In some computing systems 200, the processing unit 204 may also beconfigured to disguise consumer characteristics. Disguising consumercharacteristics may include disguising variables and/or their valuessuch that the corresponding undisguised variables and/or values may beunidentifiable to a computing system 200 that receives the correspondingdisguised characteristics. For example, if a consumer characteristic isa demographic indicating a consumer's income, where their income is oneof five predetermined ranges (e.g., less than $30,000, $30,001 to$50,000, $50,001 to $75,000, $75,001 to $100,000, and over $100,000),the processing unit 204 may disguise the values such that thecharacteristic of income may have a value of A, B, C, D, or E for aspecific encrypted account identifier. In some instances, the variableitself may also be disguised, such that a computing system 200 mayreceive the characteristic as characteristic X3 has a value of A, B, C,D, or E for each encrypted account identifier.

In some instances, processing units 204 that are configured to disguisedconsumer characteristics may also be configured to undisguise consumercharacteristics, such as in a received scoring algorithm. For example,the first computing system 106 may receive a scoring algorithm from thesecond computing system 108 that uses disguised variables and values,and the processing unit 204 of the first computing system 106 mayundisguise the variables and values prior to providing the generatedscoring algorithm to the requesting entity 104 in response to aninitially received request.

Computing systems 200 may also include a transmitting unit 206. Thetransmitting unit 206 may be configured to transmit data over one ormore networks via one or more network protocols. The transmitting unit206 may be configured to transmit data from one computing system 200 inthe processing system 102 to another computing system 200 in theprocessing system 102, such as the transmitting of disguised consumercharacteristics from the first computing system 106 to the secondcomputing system 108. The transmitting unit 206 may also be configuredto transmit data from a computing system 200 included in the processingsystem 102 to a system outside of the processing system 102, such as therequesting entity 104 and/or the payment network 112. For example, thetransmitting unit 206 of the first computing system 106 may beconfigured to transmit a generated scoring algorithm to the requestingentity 104.

Computing systems 200 may also include a memory 208. The memory 208 maybe configured to store data suitable for performing the functionsdisclosed herein. For example, the second computing system 108 may beconfigured to store received transaction data in the memory 208 prior tomapping the received transaction data to disguised consumercharacteristics. Data that may be stored in the memory 208 of variouscomputing systems 200 in the processing system 102 will be apparent topersons having skill in the relevant art.

Two System Process for Maintaining Consumer Privacy in BehavioralScoring

FIGS. 3A and 3B illustrate a process for maintaining consumer privacy inbehavioral scoring using the first computing system 106 and secondcomputing system 108 of the processing system 102.

In step 302, the first computing system 106 may identify (e.g., via theprocessing unit 204) account identifiers, wherein each accountidentifier is associated with a payment account corresponding to aconsumer. In some instances, the account identifiers may be stored inthe memory 208 of the first computing system 106. In step 304, therequesting entity 104 may identify consumer data for which behavioralscores are requested. The consumer data may include a plurality ofconsumer characteristics associated with each of a plurality ofconsumers, and an account identifier associated with each of theplurality of consumers.

In step 306, the requesting entity 104 may encrypt the accountidentifiers using a first one-way encryption and may transmit theencrypted account identifiers and corresponding consumer characteristicdata to the first computing system 108. The transmitted data may alsoinclude a scoring request, which may include a behavior predictionrequest corresponding to a purchase behavior for which the requestingentity 104 wants consumers to be scored. In step 308, the receiving unit202 of the first computing system 106 may receive the transmitted data.

In step 310, the processing unit 204 of the first computing system 106may disguise the received consumer characteristics. In some embodiments,the variables or values of the consumer characteristics may bedisguised. In other embodiments, both the variables and the values ofeach consumer characteristic may be disguised. In step 312, theprocessing unit 204 of the first computing system 106 may map theencrypted account identifiers received from the requesting entity withthe account identifiers stored in the memory 208 and identified in step302.

In step 314, the transmitting unit 206 of the first computing system 106may transmit the unencrypted account identifiers and their mappedconsumer characteristics to the second computing system 108. In step316, the receiving unit 202 of the second computing system 108 mayreceive the identifiers and mapped consumer characteristics and mayencrypt the account identifiers upon receipt using a second one-wayencryption. In some embodiments, the second one-way encryption may bedifferent than the first one-way encryption such that the encryptedaccount identifiers obtained by the second computing system 108 may bedifferent than the encrypted account identifiers transmitted to thefirst computing system 106 by the requesting entity 104.

In step 318, the receiving unit 202 of the second computing system 108may receive anonymous transaction data from the payment network 112. Theanonymous transaction data may include transaction data for a pluralityof payment transactions, wherein the transaction data for each paymenttransaction is associated with an encrypted account identifiercorresponding to a payment account involved in the respectivetransaction, where the encrypted account identifier is encrypted usingthe second one-way encryption. In step 320, the processing unit 204 ofthe second computing system 108 may match the anonymous transaction datato disguised consumer characteristics based on the respectivecorresponding encrypted account identifiers, and may score each of thematched sets of data. The scoring may be a behavioral scorecorresponding to the requested purchase behavior, and may be based onthe transaction data associated with each respective encrypted accountidentifier.

Once scores have been obtained that correspond to each encrypted accountidentifier, then, in step 322, the processing unit 204 of the secondcomputing system 108 may generate a scoring algorithm for the requestedpurchase behavior. The generated scoring algorithm may be configured tocalculate a behavioral score for a consumer based on disguised consumercharacteristics associated with that consumer. Accordingly, the scoringalgorithm may be generated based on the behavioral score for eachencrypted account identifier and the corresponding disguised consumercharacteristics.

In step 324, the processing unit 204 of the second computing system 108may be configured to calculate a behavioral score for each of therequested consumers using the generated scoring algorithm. In someinstances, the calculated score may be the same score identified foreach consumer in step 320. The transmitting unit 206 of the secondcomputing system 108 may transmit the consumer behavior scores to thefirst computing system 106.

In step 326, the receiving unit 202 of the first computing system 106may receive the consumer behavior scores. In step 328, the processingunit 204 of the first computing system 106 may generate a report of thecalculated consumer behavior scores. In some instances, the generatedreport may include the generated scoring algorithm or other informationthat may include disguised consumer characteristics. In such aninstance, step 328 may further include undisguising the disguisedconsumer characteristics. In step 330, the transmitting unit 206 of thefirst computing system 106 may transmit the generated report to therequesting entity 104 in response to the originally received scoringrequest. In step 332, the requesting entity 104 may receive thegenerated report.

In some embodiments, the requesting entity 104 may request the generatedscoring algorithm and not behavioral scores calculated using thealgorithm. In such an embodiment, steps 324 and 326 may not beperformed, and the report generated in step 328 may include thegenerated scoring algorithm transmitted from the second computing system108 to the first computing system 106, without any calculated behavioralscores included.

Three System Process for Maintaining Consumer Privacy in BehavioralScoring

FIGS. 4A-4C illustrate a process for maintaining consumer privacy inbehavioral scoring using the first computing system 106, secondcomputing system 108, and third computing system 110 of the processingsystem 102.

In step 402, the first computing system 106 may identify (e.g., via theprocessing unit 204) account identifiers, wherein each accountidentifier is associated with a payment account corresponding to aconsumer. In some instances, the account identifiers may be stored inthe memory 208 of the first computing system 106. In step 404, therequesting entity 104 may identify consumer data for which behavioralscores are requested. The consumer data may include a plurality ofconsumer characteristics associated with each of a plurality ofconsumers, and an account identifier associated with each of theplurality of consumers.

In step 406, the requesting entity 104 may encrypt the accountidentifiers using a first one-way encryption and may transmit theencrypted account identifiers and corresponding consumer characteristicdata to the first computing system 106. The transmitted data may alsoinclude a scoring request, which may include a behavior predictionrequest corresponding to a purchase behavior for which the requestingentity 104 wants consumers to be scored. In step 408, the receiving unit202 of the first computing system 106 may receive the transmitted data.

In step 410, the processing unit 204 of the first computing system 106may disguise the received consumer characteristics. In some embodiments,the variables or values of the consumer characteristics may bedisguised. In other embodiments, both the variables and the values ofeach consumer characteristic may be disguised. In step 412, theprocessing unit 204 of the first computing system 106 may map theencrypted account identifiers received from the requesting entity withthe account identifiers stored in the memory 208 and identified in step402.

In step 414, the transmitting unit 206 of the first computing system 106may transmit the unencrypted account identifiers and their mappedconsumer characteristics to the second computing system 108. In step416, the receiving unit 202 of the second computing system 108 mayreceive the unencrypted account identifiers and mapped consumercharacteristics and may encrypt the account identifiers upon receiptusing a second one-way encryption. In some embodiments, the secondone-way encryption may be different than the first one-way encryptionsuch that the encrypted account identifiers obtained by the secondcomputing system 108 may be different than the encrypted accountidentifiers transmitted to the first computing system 106 by therequesting entity 104 in step 406.

In step 418, the receiving unit 202 of the second computing system 108may receive anonymous transaction data from the payment network 112. Theanonymous transaction data may include transaction data for a pluralityof payment transactions, wherein the transaction data for each paymenttransaction is associated with an encrypted account identifiercorresponding to a payment account involved in the respectivetransaction, where the encrypted account identifier is encrypted usingthe second one-way encryption. In step 420, the processing unit 204 ofthe second computing system 108 may match the anonymous transaction datato disguised consumer characteristics based on the respectivecorresponding encrypted account identifiers, and may score each of thematched sets of data. The scoring may be a behavioral scorecorresponding to the requested purchase behavior, and may be based onthe transaction data associated with each respective encrypted accountidentifier.

In step 422, the transmitting unit 206 of the second computing system108 may transmit the calculated behavioral scores for each disguised setof consumer characteristics to the first computing system 106. In step424, the receiving unit 202 of the first computing system 106 mayreceive the calculated behavior score for each disguised set of consumercharacteristics. In step 426, the transmitting unit 206 of the firstcomputing system 106 may transmit the disguised sets of consumercharacteristics and corresponding behavior scores to the third computingsystem 110.

In step 428, the receiving unit 202 of the third computing system 110may receive the disguised consumer characteristics and correspondingbehavior scores. In step 430, the processing unit 204 of the thirdcomputing system 110 may generate a scoring algorithm for the requestedpurchase behavior. The generated scoring algorithm may be configured tocalculate a behavioral score for a consumer based on disguised consumercharacteristics associated with that consumer. Accordingly, the scoringalgorithm may be generated based on each set of disguised consumercharacteristics and the corresponding behavioral score.

In step 432 the processing unit 204 of the third computing system 110may be configured to calculate a behavioral score for each of therequested consumers using the generated scoring algorithm. In someinstances, the calculated score may be the same score previouslyidentified for each consumer and received by the third computing system110 in step 428. In step 434, the transmitting unit 206 of the thirdcomputing system 110 may transmit the consumer behavior scores to thefirst computing system 106.

In step 436, the receiving unit 202 of the first computing system 106may receive the consumer behavior scores. In step 438, the processingunit 204 of the first computing system 106 may generate an undisguisedreport of the calculated consumer behavior scores. In some instances,the generated report may include the generated scoring algorithm orother information that may include disguised consumer characteristics.In such an instance, step 438 may further include undisguising thedisguised consumer characteristics. In step 440, the transmitting unit206 of the first computing system 106 may transmit the generated reportto the requesting entity 104 in response to the originally receivedscoring request. In step 442, the requesting entity 104 may receive thegenerated report.

In some embodiments, the requesting entity 104 may request the generatedscoring algorithm and not behavioral scores calculated using thealgorithm. In such an embodiment, steps 432-436 may not be performed,and the report generated in step 438 may include the generated scoringalgorithm transmitted from the third computing system 110 to the firstcomputing system 106, without any calculated behavioral scores included.

First Exemplary Method for Maintaining Consumer Privacy in BehavioralScoring

FIG. 5 illustrates a method 500 for the maintaining of consumer privacyin behavioral scoring using two computing systems.

In step 502, a plurality of account identifiers may be stored in amemory (e.g., the memory 208) of a first computing system (e.g., thefirst computing system 106), wherein each account identifier isassociated with a payment account corresponding to a consumer. In step504, transmitted data may be received by a receiver (e.g., the receivingunit 202) of the first computing system 106, wherein the transmitteddata includes at least a behavior prediction request and a data fileincluding at least a plurality of first encrypted account identifiers,wherein each first encrypted account identifier is encrypted using afirst one-way encryption and is associated with a payment accountcorresponding to a consumer, and further including, for each firstencrypted account identifier, a set of consumer characteristicsassociated with the consumer corresponding to the associated paymentaccount.

In step 506, each set of consumer characteristics included in thereceived data file may be disguised by a processor (e.g., the processingunit 204) of the first computing system 106 such that the respective setof consumer characteristics is not personally identifiable. In someembodiments, the set of consumer characteristics may include variablesand values, and disguising each set of consumer characteristics mayinclude disguising at least the included variables. In step 508, each ofthe plurality of first encrypted account identifiers and correspondingdisguised set of consumer characteristics may be mapped to an accountidentifier of the plurality of account identifiers in the memory 208 ofthe first computing system 106.

In step 510, at least each account identifier and mapped first encryptedaccount identifier and corresponding disguised set of consumercharacteristics may be transmitted by a transmitter (e.g., thetransmitting unit 206) of the first computing system 106 to a secondcomputing system (e.g., the second computing system 108), wherein areceiver (e.g., the receiving unit 202) of the second computing system108 is configured to encrypt each account identifier into a secondencrypted account identifier using a second one-way encryption uponreceipt. In step 512, a plurality of transaction data entries may bereceived by the receiving unit 202 of the second computing system 108,wherein each transaction data entry includes data related to a paymenttransaction including at least a second encrypted account identifier andtransaction data.

In step 514, an algorithm may be generated by a processor (e.g., theprocessing unit 204) of the second computing system 108 configured tocalculate a behavior prediction score corresponding to the behaviorprediction request using disguised consumer characteristic values,wherein the generated algorithm is based on at least the transactiondata included in each received transaction data entry and the disguisedset of consumer characteristics mapped to the second encrypted accountidentifier included in the respective transaction data entry, whereinthe second computing system 108 does not receive any unencrypted accountidentifiers, any undisguised consumer characteristics, or any personallyidentifiable information.

In one embodiment, the method 500 may further include calculating, bythe processor 204 of the second computing system 108, a behaviorprediction score for each first encrypted account identifier byapplication of the corresponding disguised set of consumercharacteristics to the generated algorithm, and transmitting, by atransmitter (e.g., transmitting unit 206) of the second computing system108, at least the calculated behavior prediction score for each firstencrypted account identifier and the corresponding first encryptedaccount identifier. In a further embodiment, the calculated behaviorprediction score for each first encrypted account identifier and thecorresponding first encrypted account identifier may be transmitted tothe first computing system 106, and the method 500 may even furtherinclude: receiving, by the receiving unit 202 of the first computingsystem 106, the calculated behavior prediction score for each firstencrypted account identifier; and transmitting, by the transmitting unit206 of the first computing system 106, the calculated behaviorprediction score for each first encrypted account identifier and thecorresponding first encrypted account identifier in response to thereceived transmitted data.

In some embodiments, the method 500 may further include transmitting, bythe transmitting unit 206 of the second computing system 106, thegenerated algorithm. In a further embodiment, the generated algorithmmay be transmitted in response to the received transmitted data. Inanother further embodiment, the method 500 may even further include:receiving, by the receiver of the first computing system, the generatedalgorithm; modifying, by the processor of the first computing system,the generated algorithm such that the modified algorithm is configuredto calculate a behavior prediction score corresponding to the behaviorprediction request using undisguised consumer characteristic values; andtransmitting, by the transmitter of the first computing system, at leastthe modified algorithm in response to the received transmitted data.

Second Exemplary Method for Maintaining Consumer Privacy in BehavioralScoring

FIG. 6 illustrates a method 600 for the maintaining of consumer privacyin behavioral scoring using three computing systems.

In step 602, a plurality of account identifiers may be stored in amemory (e.g., the memory 208) of a first computing system (e.g., thefirst computing system 106), wherein each account identifier isassociated with a payment account corresponding to a consumer. In step604, transmitted data may be received by a receiver (e.g., the receivingunit 202) of the first computing system 106, wherein the transmitteddata includes at least a behavior prediction request and a data fileincluding at least a plurality of first encrypted account identifiers,wherein each first encrypted account identifier is encrypted using afirst one-way encryption and is associated with a payment accountcorresponding to a consumer, and further including, for each firstencrypted account identifier, a set of consumer characteristicsassociated with the consumer corresponding to the associated paymentaccount.

In step 606, each set of consumer characteristics included in thereceived data file may be disguised by a processor (e.g., the processingunit 204) of the first computing system 106 such that the respective setof consumer characteristics is not personally identifiable. In someembodiments, the set of consumer characteristics may include variablesand values, and disguising each set of consumer characteristics mayinclude disguising at least the included variables. In step 608, each ofthe plurality of first encrypted account identifiers and correspondingdisguised set of consumer characteristics may be mapped in the memory(e.g., memory 208) of the first computing system 106, to an accountidentifier of the plurality of account identifiers

In step 610, at least each account identifier and mapped first encryptedaccount identifier may be transmitted, by a transmitter (e.g., thetransmitting unit 206) of the first computing system 106 to a secondcomputing system (e.g., the second computing system 108), wherein areceiver (e.g., receiving unit 202) of the second computing system 108is configured to encrypt each account identifier into a second encryptedaccount identifier using a second one-way encryption upon receipt. Instep 612, a plurality of transaction data entries may be received by thereceiving unit 202 of the second computing system 108, wherein eachtransaction data entry includes data related to a payment transactionincluding at least a second encrypted account identifier and transactiondata.

In step 614, a behavior prediction score corresponding to the behaviorprediction request may be calculated, by a processor (e.g., theprocessing unit 204) of the second computing system 108, for each secondencrypted account identifier based on at least the transaction dataincluded in each transaction data entry including the respective secondencrypted account identifier. In step 616, the calculated behaviorprediction score for each second encrypted account identifier may betransmitted by a transmitter (e.g., transmitting unit 206) of the secondcomputing system 108, wherein the second computing system 108 does notreceive any unencrypted account identifiers, any undisguised consumercharacteristics, or any personally identifiable information.

In one embodiment, the method 600 may further include: receiving, by areceiver (e.g., receiving unit 202) of a third computing system (e.g.,the third computing system 110), at least the calculated behaviorprediction score for each second encrypted account identifier and thefirst encrypted account identifier and disguised set of consumercharacteristics mapped to the account identifier corresponding to therespective second encrypted account identifier; and generating, by aprocessor (e.g., processing unit 204) of the third computing system 110,an algorithm configured to calculate a behavior prediction scorecorresponding to the behavior prediction request using disguisedconsumer characteristic values, wherein the generated algorithm is basedon at least the behavior prediction score and the disguised set ofconsumer characteristics for each first encrypted account identifier,wherein the third computing system 110 does not receive any unencryptedaccount identifiers, any undisguised consumer characteristics, or anypersonally identifiable information.

In a further embodiment, the method 600 may even further include:calculating, by the processor 204 of the third computing system 110, abehavior prediction score for each first encrypted account identifier byapplication of the corresponding disguised set of consumercharacteristics to the generated algorithm; and transmitting, by atransmitter (e.g., transmitting unit 206) of the third computing system110, at least the calculated behavior prediction score for each firstencrypted account identifier and the corresponding first encryptedaccount identifier. In another further embodiment, the calculatedbehavior prediction score for each first encrypted account identifierand the corresponding first encrypted account identifier may betransmitted to the first computing system 106, and the method 600 mayfurther include: receiving, by the receiving unit 202 of the firstcomputing system 106, the calculated behavior prediction score for eachfirst encrypted account identifier; and transmitting, by thetransmitting unit 206 of the first computing system 106, the calculatedbehavior prediction score for each first encrypted account identifierand the corresponding first encrypted account identifier in response tothe received transmitted data.

In one further embodiment, the method 600 may also include transmitting,by the transmitting unit 206 of the third computing system 110, at leastthe generated algorithm. In an even further embodiment, the generatedalgorithm may be transmitted in response to the received transmitteddata. In another even further embodiment, the method 600 may furtherinclude: receiving, by the receiving unit 202 of the first computingsystem 106, the generated algorithm; modifying, by the processor 204 ofthe first computing system 106, the generated algorithm such that themodified algorithm is configured to calculate a behavior predictionscore corresponding to the behavior prediction request using undisguisedconsumer characteristic values; and transmitting, by the transmittingunit 206 of the first computing system 106, at least the modifiedalgorithm in response to the received transmitted data.

Computer System Architecture

FIG. 7 illustrates a computer system 700 in which embodiments of thepresent disclosure, or portions thereof, may be implemented ascomputer-readable code. For example, the first computing system 106,second computing system 108, and third computing system 110 of FIG. 1and the computing system 200 of FIG. 2 may be implemented in thecomputer system 700 using hardware, software, firmware, non-transitorycomputer readable media having instructions stored thereon, or acombination thereof and may be implemented in one or more computersystems or other processing systems. Hardware, software, or anycombination thereof may embody modules and components used to implementthe methods of FIGS. 3A, 3B, 4A-4C, 5, and 6.

If programmable logic is used, such logic may execute on a commerciallyavailable processing platform or a special purpose device. A personhaving ordinary skill in the art may appreciate that embodiments of thedisclosed subject matter can be practiced with various computer systemconfigurations, including multi-core multiprocessor systems,minicomputers, mainframe computers, computers linked or clustered withdistributed functions, as well as pervasive or miniature computers thatmay be embedded into virtually any device. For instance, at least oneprocessor device and a memory may be used to implement the abovedescribed embodiments.

A processor unit or device as discussed herein may be a singleprocessor, a plurality of processors, or combinations thereof. Processordevices may have one or more processor “cores.” The terms “computerprogram medium,” “non-transitory computer readable medium,” and“computer usable medium” as discussed herein are used to generally referto tangible media such as a removable storage unit 718, a removablestorage unit 722, and a hard disk installed in hard disk drive 712.

Various embodiments of the present disclosure are described in terms ofthis example computer system 700. After reading this description, itwill become apparent to a person skilled in the relevant art how toimplement the present disclosure using other computer systems and/orcomputer architectures. Although operations may be described as asequential process, some of the operations may in fact be performed inparallel, concurrently, and/or in a distributed environment, and withprogram code stored locally or remotely for access by single ormulti-processor machines. In addition, in some embodiments the order ofoperations may be rearranged without departing from the spirit of thedisclosed subject matter.

Processor device 704 may be a special purpose or a general purposeprocessor device. The processor device 704 may be connected to acommunications infrastructure 706, such as a bus, message queue,network, multi-core message-passing scheme, etc. The network may be anynetwork suitable for performing the functions as disclosed herein andmay include a local area network (LAN), a wide area network (WAN), awireless network (e.g., WiFi), a mobile communication network, asatellite network, the Internet, fiber optic, coaxial cable, infrared,radio frequency (RF), or any combination thereof. Other suitable networktypes and configurations will be apparent to persons having skill in therelevant art. The computer system 700 may also include a main memory 708(e.g., random access memory, read-only memory, etc.), and may alsoinclude a secondary memory 710. The secondary memory 710 may include thehard disk drive 712 and a removable storage drive 714, such as a floppydisk drive, a magnetic tape drive, an optical disk drive, a flashmemory, etc.

The removable storage drive 714 may read from and/or write to theremovable storage unit 718 in a well-known manner. The removable storageunit 718 may include a removable storage media that may be read by andwritten to by the removable storage drive 714. For example, if theremovable storage drive 714 is a floppy disk drive or universal serialbus port, the removable storage unit 718 may be a floppy disk orportable flash drive, respectively. In one embodiment, the removablestorage unit 718 may be non-transitory computer readable recordingmedia.

In some embodiments, the secondary memory 710 may include alternativemeans for allowing computer programs or other instructions to be loadedinto the computer system 700, for example, the removable storage unit722 and an interface 720. Examples of such means may include a programcartridge and cartridge interface (e.g., as found in video gamesystems), a removable memory chip (e.g., EEPROM, PROM, etc.) andassociated socket, and other removable storage units 722 and interfaces720 as will be apparent to persons having skill in the relevant art.

Data stored in the computer system 700 (e.g., in the main memory 708and/or the secondary memory 710) may be stored on any type of suitablecomputer readable media, such as optical storage (e.g., a compact disc,digital versatile disc, Blu-ray disc, etc.) or magnetic tape storage(e.g., a hard disk drive). The data may be configured in any type ofsuitable database configuration, such as a relational database, astructured query language (SQL) database, a distributed database, anobject database, etc. Suitable configurations and storage types will beapparent to persons having skill in the relevant art.

The computer system 700 may also include a communications interface 724.The communications interface 724 may be configured to allow software anddata to be transferred between the computer system 700 and externaldevices. Exemplary communications interfaces 724 may include a modem, anetwork interface (e.g., an Ethernet card), a communications port, aPCMCIA slot and card, etc. Software and data transferred via thecommunications interface 724 may be in the form of signals, which may beelectronic, electromagnetic, optical, or other signals as will beapparent to persons having skill in the relevant art. The signals maytravel via a communications path 726, which may be configured to carrythe signals and may be implemented using wire, cable, fiber optics, aphone line, a cellular phone link, a radio frequency link, etc.

The computer system 700 may further include a display interface 702. Thedisplay interface 702 may be configured to allow data to be transferredbetween the computer system 700 and external display 730. Exemplarydisplay interfaces 702 may include high-definition multimedia interface(HDMI), digital visual interface (DVI), video graphics array (VGA), etc.The display 730 may be any suitable type of display for displaying datatransmitted via the display interface 702 of the computer system 700,including a cathode ray tube (CRT) display, liquid crystal display(LCD), light-emitting diode (LED) display, capacitive touch display,thin-film transistor (TFT) display, etc.

Computer program medium and computer usable medium may refer tomemories, such as the main memory 708 and secondary memory 710, whichmay be memory semiconductors (e.g., DRAMs, etc.). These computer programproducts may be means for providing software to the computer system 700.Computer programs (e.g., computer control logic) may be stored in themain memory 708 and/or the secondary memory 710. Computer programs mayalso be received via the communications interface 724. Such computerprograms, when executed, may enable computer system 700 to implement thepresent methods as discussed herein. In particular, the computerprograms, when executed, may enable processor device 704 to implementthe methods illustrated by FIGS. 3A, 3B, 4A-4C, 5, and 6, as discussedherein. Accordingly, such computer programs may represent controllers ofthe computer system 700. Where the present disclosure is implementedusing software, the software may be stored in a computer program productand loaded into the computer system 700 using the removable storagedrive 714, interface 720, and hard disk drive 712, or communicationsinterface 724.

Techniques consistent with the present disclosure provide, among otherfeatures, systems and methods for maintaining consumer privacy inbehavioral scoring. While various exemplary embodiments of the disclosedsystem and method have been described above it should be understood thatthey have been presented for purposes of example only, not limitations.It is not exhaustive and does not limit the disclosure to the preciseform disclosed. Modifications and variations are possible in light ofthe above teachings or may be acquired from practicing of thedisclosure, without departing from the breadth or scope.

What is claimed is:
 1. A method for maintaining consumer privacy inbehavioral scoring, comprising: storing, in a memory of a firstcomputing system, a plurality of account identifiers, wherein eachaccount identifier is associated with a payment account corresponding toa consumer; receiving, by a receiver of the first computing system,transmitted data, wherein the transmitted data includes at least abehavior prediction request and a data file including at least aplurality of first encrypted account identifiers, wherein each firstencrypted account identifier is encrypted using a first one-wayencryption and is associated with a payment account corresponding to aconsumer, and further including, for each first encrypted accountidentifier, a set of consumer characteristics associated with theconsumer corresponding to the associated payment account; disguising, bya processor of the first computing system, each set of consumercharacteristics included in the received data file such that therespective set of consumer characteristics is not personallyidentifiable; mapping, in the memory of the first computing system, eachof the plurality of first encrypted account identifiers andcorresponding disguised set of consumer characteristics to an accountidentifier of the plurality of account identifiers; transmitting, by atransmitter of the first computing system, at least each accountidentifier and mapped first encrypted account identifier andcorresponding disguised set of consumer characteristics to a secondcomputing system, wherein a receiver of the second computing system isconfigured to encrypt each account identifier into a second encryptedaccount identifier using a second one-way encryption upon receipt;receiving, by the receiver of the second computing system, a pluralityof transaction data entries, wherein each transaction data entryincludes data related to a payment transaction including at least asecond encrypted account identifier and transaction data; generating, bya processor of the second computing system, an algorithm configured tocalculate a behavior prediction score corresponding to the behaviorprediction request using disguised consumer characteristic values,wherein the generated algorithm is based on at least the transactiondata included in each received transaction data entry and the disguisedset of consumer characteristics mapped to the second encrypted accountidentifier included in the respective transaction data entry, whereinthe second computing system does not receive any unencrypted accountidentifiers, any undisguised consumer characteristics, or any personallyidentifiable information.
 2. The method of claim 1, further comprising:calculating, by the processor of the second computing system, a behaviorprediction score for each first encrypted account identifier byapplication of the corresponding disguised set of consumercharacteristics to the generated algorithm; and transmitting, by atransmitter of the second computing system, at least the calculatedbehavior prediction score for each first encrypted account identifierand the corresponding first encrypted account identifier.
 3. The methodof claim 2, wherein the calculated behavior prediction score for eachfirst encrypted account identifier and the corresponding first encryptedaccount identifier are transmitted to the first computing system, andthe method further comprises: receiving, by the receiver of the firstcomputing system, the calculated behavior prediction score for eachfirst encrypted account identifier; and transmitting, by the transmitterof the first computing system, the calculated behavior prediction scorefor each first encrypted account identifier and the corresponding firstencrypted account identifier in response to the received transmitteddata.
 4. The method of claim 1, further comprising: transmitting, by atransmitter of the second computing system, at least the generatedalgorithm.
 5. The method of claim 4, wherein the generated algorithm istransmitted in response to the received transmitted data.
 6. The methodof claim 4, further comprising: receiving, by the receiver of the firstcomputing system, the generated algorithm; modifying, by the processorof the first computing system, the generated algorithm such that themodified algorithm is configured to calculate a behavior predictionscore corresponding to the behavior prediction request using undisguisedconsumer characteristic values; and transmitting, by the transmitter ofthe first computing system, at least the modified algorithm in responseto the received transmitted data.
 7. The method of claim 1, wherein theset of consumer characteristics includes variables and values, anddisguising each set of consumer characteristics includes disguising atleast the included variables.
 8. A method for maintaining consumerprivacy in behavioral prediction scoring, comprising: storing, in amemory of a first computing system, a plurality of account identifiers,wherein each account identifier is associated with a payment accountcorresponding to a consumer; receiving, by a receiver of a firstcomputing system, transmitted data, wherein the transmitted dataincludes at least a behavior prediction request and a data fileincluding at least a plurality of first encrypted account identifiers,wherein each first encrypted account identifier is encrypted using afirst one-way encryption and is associated with a payment accountcorresponding to a consumer, and further including, for each firstencrypted account identifier, a set of consumer characteristicsassociated with the consumer corresponding to the associated paymentaccount; disguising, by a processor of the first computing system, eachset of consumer characteristics included in the received data file suchthat the respective set of consumer characteristics is not personallyidentifiable; mapping, in the memory of the first computing system, eachof the plurality of first encrypted account identifiers andcorresponding disguised set of consumer characteristics to an accountidentifier of the plurality of account identifiers; transmitting, by atransmitter of the first computing system, at least each accountidentifier and mapped first encrypted account identifier to a secondcomputing system, wherein a receiver of the second computing system isconfigured to encrypt each account identifier into a second encryptedaccount identifier using a second one-way encryption upon receipt;receiving, by the receiver of the second computing system, a pluralityof transaction data entries, wherein each transaction data entryincludes data related to a payment transaction including at least asecond encrypted account identifier and transaction data; calculating,by a processor of the second computing system, a behavior predictionscore corresponding to the behavior prediction request for each secondencrypted account identifier based on at least the transaction dataincluded in each transaction data entry including the respective secondencrypted account identifier; and transmitting, by a transmitter of thesecond computing system, the calculated behavior prediction score foreach second encrypted account identifier, wherein the second computingsystem does not receive any unencrypted account identifiers, anyundisguised consumer characteristics, or any personally identifiableinformation.
 9. The method of claim 8, further comprising: receiving, bya receiver of a third computing system, at least the calculated behaviorprediction score for each second encrypted account identifier and thefirst encrypted account identifier and disguised set of consumercharacteristics mapped to the account identifier corresponding to therespective second encrypted account identifier; and generating, by aprocessor of the third computing system, an algorithm configured tocalculate a behavior prediction score corresponding to the behaviorprediction request using disguised consumer characteristic values,wherein the generated algorithm is based on at least the behaviorprediction score and the disguised set of consumer characteristics foreach first encrypted account identifier, wherein the third computingsystem does not receive any unencrypted account identifiers, anyundisguised consumer characteristics, or any personally identifiableinformation.
 10. The method of claim 9, further comprising: calculating,by the processor of the third computing system, a behavior predictionscore for each first encrypted account identifier by application of thecorresponding disguised set of consumer characteristics to the generatedalgorithm; and transmitting, by a transmitter of the third computingsystem, at least the calculated behavior prediction score for each firstencrypted account identifier and the corresponding first encryptedaccount identifier.
 11. The method of claim 9, wherein the calculatedbehavior prediction score for each first encrypted account identifierand the corresponding first encrypted account identifier are transmittedto the first computing system, and the method further comprises:receiving, by the receiver of the first computing system, the calculatedbehavior prediction score for each first encrypted account identifier;and transmitting, by the transmitter of the first computing system, thecalculated behavior prediction score for each first encrypted accountidentifier and the corresponding first encrypted account identifier inresponse to the received transmitted data.
 12. The method of claim 9,further comprising: transmitting, by a transmitter of the thirdcomputing system, at least the generated algorithm.
 13. The method ofclaim 12, wherein the generated algorithm is transmitted in response tothe received transmitted data.
 14. The method of claim 12, furthercomprising: receiving, by the receiver of the first computing system,the generated algorithm; modifying, by the processor of the firstcomputing system, the generated algorithm such that the modifiedalgorithm is configured to calculate a behavior prediction scorecorresponding to the behavior prediction request using undisguisedconsumer characteristic values; and transmitting, by the transmitter ofthe first computing system, at least the modified algorithm in responseto the received transmitted data.
 15. The method of claim 8, wherein theset of consumer characteristics includes variables and values, anddisguising each set of consumer characteristics includes disguising atleast the included variables.
 16. A system for maintaining consumerprivacy in behavioral prediction scoring, comprising: a first computingsystem; and a second computing system, wherein the first computingsystem includes a memory configured to store a plurality of accountidentifiers, wherein each account identifier is associated with apayment account corresponding to a consumer, a receiver configured toreceive transmitted data, wherein the transmitted data includes at leasta behavior prediction request and a data file including at least aplurality of first encrypted account identifiers, wherein each firstencrypted account identifier is encrypted using a first one-wayencryption and is associated with a payment account corresponding to aconsumer, and further including, for each first encrypted accountidentifier, a set of consumer characteristics associated with theconsumer corresponding to the associated payment account, a processorconfigured to disguise each set of consumer characteristics included inthe received data file such that the respective set of consumercharacteristics is not personally identifiable, and map each of theplurality of first encrypted account identifiers and correspondingdisguised set of consumer characteristics to an account identifier ofthe plurality of account identifiers, and a transmitter configured totransmit at least each account identifier and mapped first encryptedaccount identifier and corresponding disguised set of consumercharacteristics to a second computing system, wherein a receiver of thesecond computing system is configured to encrypt each account identifierinto a second encrypted account identifier using a second one-wayencryption upon receipt, the second computing system includes a receiverconfigured to receive a plurality of transaction data entries, whereineach transaction data entry includes data related to a paymenttransaction including at least a second encrypted account identifier andtransaction data, and a processor configured to generate an algorithmconfigured to calculate a behavior prediction score corresponding to thebehavior prediction request using disguised consumer characteristicvalues, wherein the generated algorithm is based on at least thetransaction data included in each received transaction data entry andthe disguised set of consumer characteristics mapped to the secondencrypted account identifier included in the respective transaction dataentry, and the second computing system does not receive any unencryptedaccount identifiers, any undisguised consumer characteristics, or anypersonally identifiable information.
 17. The system of claim 16, whereinthe processor of the second computing system is further configured tocalculate a behavior prediction score for each first encrypted accountidentifier by application of the corresponding disguised set of consumercharacteristics to the generated algorithm, and the second computingsystem further includes a transmitter configured to transmit at leastthe calculated behavior prediction score for each first encryptedaccount identifier and the corresponding first encrypted accountidentifier.
 18. The system of claim 17, wherein the calculated behaviorprediction score for each first encrypted account identifier and thecorresponding first encrypted account identifier are transmitted to thefirst computing system, the receiver of the first computing system isfurther configured to receive the calculated behavior prediction scorefor each first encrypted account identifier, and the transmitter of thefirst computing system is further configured to transmit the calculatedbehavior prediction score for each first encrypted account identifierand the corresponding first encrypted account identifier in response tothe received transmitted data.
 19. The system of claim 16, wherein thesecond computing system further includes a transmitter configured totransmit at least the generated algorithm.
 20. The system of claim 19,wherein the generated algorithm is transmitted in response to thereceived transmitted data.
 21. The system of claim 19, wherein thereceiver of the first computing system is further configured to receivethe generated algorithm, the processor of the first computing system isfurther configured to modify the generated algorithm such that themodified algorithm is configured to calculate a behavior predictionscore corresponding to the behavior prediction request using undisguisedconsumer characteristic values, and the transmitter of the firstcomputing system is further configured to transmit at least the modifiedalgorithm in response to the received transmitted data.
 22. The systemof claim 16, wherein the set of consumer characteristics includesvariables and values, and disguising each set of consumercharacteristics includes disguising at least the included variables. 23.A system for maintaining consumer privacy in behavioral predictionscoring, comprising: a first computing system; and a second computingsystem, wherein the first computing system includes a memory configuredto store a plurality of account identifiers, wherein each accountidentifier is associated with a payment account corresponding to aconsumer, a receiver configured to receive transmitted data, wherein thetransmitted data includes at least a behavior prediction request and adata file including at least a plurality of first encrypted accountidentifiers, wherein each first encrypted account identifier isencrypted using a first one-way encryption and is associated with apayment account corresponding to a consumer, and further including, foreach first encrypted account identifier, a set of consumercharacteristics associated with the consumer corresponding to theassociated payment account, a processor configured to disguise each setof consumer characteristics included in the received data file such thatthe respective set of consumer characteristics is not personallyidentifiable, and map, in the memory of the first computing system, eachof the plurality of first encrypted account identifiers andcorresponding disguised set of consumer characteristics to an accountidentifier of the plurality of account identifiers, and a transmitterconfigured to transmit at least each account identifier and mapped firstencrypted account identifier to a second computing system, wherein areceiver of the second computing system is configured to encrypt eachaccount identifier into a second encrypted account identifier using asecond one-way encryption upon receipt, the second computing systemincludes a receiver configured to receive a plurality of transactiondata entries, wherein each transaction data entry includes data relatedto a payment transaction including at least a second encrypted accountidentifier and transaction data, a processor configured to calculate abehavior prediction score corresponding to the behavior predictionrequest for each second encrypted account identifier based on at leastthe transaction data included in each transaction data entry includingthe respective second encrypted account identifier, and a transmitterconfigured to transmit the calculated behavior prediction score for eachsecond encrypted account identifier, and the second computing systemdoes not receive any unencrypted account identifiers, any undisguisedconsumer characteristics, or any personally identifiable information.24. The system of claim 23, further comprising: a third computingsystem, wherein the third computing system includes a receiverconfigured to receive at least the calculated behavior prediction scorefor each second encrypted account identifier and the first encryptedaccount identifier and disguised set of consumer characteristics mappedto the account identifier corresponding to the respective secondencrypted account identifier, and a processor configured to generate analgorithm configured to calculate a behavior prediction scorecorresponding to the behavior prediction request using disguisedconsumer characteristic values, wherein the generated algorithm is basedon at least the behavior prediction score and the disguised set ofconsumer characteristics for each first encrypted account identifier,and the third computing system does not receive any unencrypted accountidentifiers, any undisguised consumer characteristics, or any personallyidentifiable information.
 25. The system of claim 24, wherein theprocessor of the third computing system is further configured tocalculate a behavior prediction score for each first encrypted accountidentifier by application of the corresponding disguised set of consumercharacteristics to the generated algorithm, and the third computingsystem further includes a transmitter configured to transmit at leastthe calculated behavior prediction score for each first encryptedaccount identifier and the corresponding first encrypted accountidentifier.
 26. The system of claim 24, wherein the calculated behaviorprediction score for each first encrypted account identifier and thecorresponding first encrypted account identifier are transmitted to thefirst computing system, the receiver of the first computing system isfurther configured to receive the calculated behavior prediction scorefor each first encrypted account identifier, and the transmitter of thefirst computing system is further configured to transmit the calculatedbehavior prediction score for each first encrypted account identifierand the corresponding first encrypted account identifier in response tothe received transmitted data.
 27. The system of claim 23, wherein thethird computing system further includes a transmitter configured totransmit at least the generated algorithm.
 28. The system of claim 27,wherein the generated algorithm is transmitted in response to thereceived transmitted data.
 29. The system of claim 27, wherein thereceiver of the first computing system is further configured to receivethe generated algorithm, the processor of the first computing system isfurther configured to modify the generated algorithm such that themodified algorithm is configured to calculate a behavior predictionscore corresponding to the behavior prediction request using undisguisedconsumer characteristic values, and the transmitter of the firstcomputing system is further configured to transmit at least the modifiedalgorithm in response to the received transmitted data.
 30. The systemof claim 23, wherein the set of consumer characteristics includesvariables and values, and disguising each set of consumercharacteristics includes disguising at least the included variables.